import numpy as np
from sigmoid import sigmoid

def identity_function(x):
	return x

def init_network():
	network = {}
	network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
	network['b1'] = np.array([0.1, 0.2, 0.3])
	network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5],[0.3, 0.6]])
	network['b2'] = np.array([0.1, 0.2])
	network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])
	network['b3'] = np.array([0.1, 0.2])
	return network

def forwark(network, x):
	W1, W2, W3 = network['W1'], network['W2'], network['W3']
	b1, b2, b3 = network['b1'], network['b2'], network['b3']
	
	a1 = np.dot(x, W1) + b1
	z1 = sigmoid(a1)
	a2 = np.dot(z1, W2) + b2
	z2 = sigmoid(a2)
	a3 = np.dot(z2, W3) + b3
	y = identity_function(a3)
	
	return y
	
network = init_network()
x = np.array([1.0, 0.5])
y = forwark(network, x)
print(y)
